# Preferential Batch Bayesian Optimization

arXiv: https://arxiv.org/abs/2003.11435 · HTML: https://mlsanity.com/paper/2003.11435/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization](https://mlsanity.com/paper/2303.15746/) (2023)
- [Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes](https://mlsanity.com/paper/2302.01513/) (2023)
- [PABBO: Preferential Amortized Black-Box Optimization](https://mlsanity.com/paper/2503.00924/) (2025)
- [Efficient Exploration in Binary and Preferential Bayesian Optimization](https://mlsanity.com/paper/2110.09361/) (2021)
- [Learning Choice Functions with Gaussian Processes](https://mlsanity.com/paper/2302.00406/) (2023)
- [Optimizing Algorithms From Pairwise User Preferences](https://mlsanity.com/paper/2308.04571/) (2023)
- [Preferential Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2406.14699/) (2024)
- [Choice functions based multi-objective Bayesian optimisation](https://mlsanity.com/paper/2110.08217/) (2021)
- [The Impact of Expertise in the Loop for Exploring Machine Rationality](https://mlsanity.com/paper/2302.05665/) (2023)
- [Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization](https://mlsanity.com/paper/2405.14657/) (2024)
- [Bayesian Optimization with Preference Exploration using a Monotonic Neural Network Ensemble](https://mlsanity.com/paper/2501.18792/) (2025)
- [Efficient Contextual Preferential Bayesian Optimization with Historical Examples](https://mlsanity.com/paper/2208.10300/) (2022)
- [Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit](https://mlsanity.com/paper/2311.14003/) (2023)
- [Poisson Process for Bayesian Optimization](https://mlsanity.com/paper/2402.02687/) (2024)
- [DT-PBO: an Interpretable Tree-based Surrogate Model for Preferential Bayesian Optimization](https://mlsanity.com/paper/2512.14263/) (2025)
- [A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback](https://mlsanity.com/paper/2604.25025/) (2026)
- [Elicitation-Augmented Bayesian Optimization](https://mlsanity.com/paper/2605.12079/) (2026)
- [Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization](https://mlsanity.com/paper/2610.14757/) (2026)

## Abstract (arXiv)

Most research in Bayesian optimization (BO) has focused on direct feedback scenarios, where one has access to exact values of some expensive-to-evaluate objective. This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems. However, in domains such as modelling human preferences, A/B tests, or recommender systems, there is a need for methods that can replace direct feedback with preferential feedback, obtained via rankings or pairwise comparisons. In this work, we present preferential batch Bayesian optimization (PBBO), a new framework that allows finding the optimum of a latent function of interest, given any type of parallel preferential feedback for a group of two or more points. We do so by using a Gaussian process model with a likelihood specially designed to enable parallel and efficient data collection mechanisms, which are key in modern machine learning. We show how the acquisitions developed under this framework generalize and augment previous approaches in Bayesian optimization, expanding the use of these techniques to a wider range of domains. An extensive simulation study shows the benefits of this approach, both with simulated functions and four real data sets.